Zero-Shot Image Classification
Transformers
Safetensors
sentence-transformers
mllama
image-text-to-text
mmeb
text-generation-inference
Instructions to use intfloat/mmE5-mllama-11b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use intfloat/mmE5-mllama-11b-instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="intfloat/mmE5-mllama-11b-instruct") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("intfloat/mmE5-mllama-11b-instruct") model = AutoModelForMultimodalLM.from_pretrained("intfloat/mmE5-mllama-11b-instruct") - sentence-transformers
How to use intfloat/mmE5-mllama-11b-instruct with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("intfloat/mmE5-mllama-11b-instruct") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Fix task tag
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by merve HF Staff - opened
README.md
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library_name: transformers
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license: mit
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pipeline_tag:
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---
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## mmE5-mllama-11b-instruct
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journal={arXiv preprint arXiv:2502.08468},
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year={2025}
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}
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```
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- fr
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library_name: transformers
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license: mit
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pipeline_tag: zero-shot-image-classification
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---
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## mmE5-mllama-11b-instruct
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journal={arXiv preprint arXiv:2502.08468},
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year={2025}
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}
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```
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